Spectral image deconvolution using sensor models

Travis F. Blake; Stephen C. Cain; Matthew E. Goda; Kenneth J. Jerkatis

doi:10.1117/12.613645

23 August 2005 Spectral image deconvolution using sensor models

Travis F. Blake, Stephen C. Cain, Matthew E. Goda, Kenneth J. Jerkatis

Proceedings Volume 5896, Unconventional Imaging; 589606 (2005) https://doi.org/10.1117/12.613645
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

This research develops a Model-based Spectral Image Deconvolution (MBSID) algorithm based on statistical estimation to spectrally deconvolve images collected from a spectral imaging sensor. The development of the algorithm requires only two key elements, 1) the statistics of the light arrival and 2) an in-depth knowledge of the spectral imaging sensor. With these two elements, the MBSID algorithm can, through image post-processing, dramatically increase the spectral resolution of the images as well as give insight into the performance of the imaging sensor itself. While MBSID algorithms can be developed for any spectral imaging system, for this research an algorithm is developed for ASIS (AEOS Spectral Imaging Sensor), a new spectral imaging sensor installed with the 3.6m Advanced Electro-Optical System (AEOS) telescope at the Maui Space Surveillance Complex (MSSC). The primary purpose of ASIS is to take spatial and spectral images of space objects. The stringent requirements associated with imaging these objects, especially the low-light levels and object motion, required a sensor design with less spectral resolution than required for image analysis. However, by applying MBSID to the collected data, the sensor will be capable of achieving a much higher spectral resolution, allowing for better spectral analysis of the space object.

Citation Download Citation

Travis F. Blake, Stephen C. Cain, Matthew E. Goda, and Kenneth J. Jerkatis "Spectral image deconvolution using sensor models", Proc. SPIE 5896, Unconventional Imaging, 589606 (23 August 2005); https://doi.org/10.1117/12.613645

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available